Data Engineer
A Data Engineer designs, builds, and operates the pipelines that move data from source systems — product databases, third-party APIs, event streams — into a warehouse or lakehouse that other people and systems can actually rely on. The center of gravity is data movement, transformation, and platform reliability, not modeling or analysis on top of the data. Python and SQL are the baseline (each shows up in roughly 4 out of 5 postings), Spark and a cloud warehouse (Snowflake, Databricks, BigQuery, Redshift) are the default stack, and at this point AI/ML fluency isn't a specialty add-on — some flavor of it (feeding ML pipelines, using AI coding tools day to day, building agentic pipeline components) shows up in nearly 4 out of 5 postings too, at companies as different as Capital One and Nvidia. It's commonly confused with "data scientist but more technical" — reality: these are different jobs with different outputs, and the titles get used inconsistently enough across companies that the confusion is understandable, not a sign you're missing something obvious.
What matters most for this role
Added as a real domain-fluency gate for this archetype, mirroring physical_constraint_engineering/adversarial_threat_modeling's pattern — building and operating production data pipelines/warehouse infrastructure is the literal, hands-on core of the job.
Senior/staff engineers own platform-wide architecture (warehouse migration, streaming platform) meant to hold up as the company scales.
Formal on-call/pager rotations documented (Uber: dedicated on-call for 20,000+ pipelines); 87%-blame finding centers on firefighting and being blamed when data breaks.
'Diary of a Data Engineer' account shows a nominal one-day task spanning schema discovery, Spark/JVM debugging, and cluster-contention troubleshooting over three weeks.
Practitioner accounts describe checking a monitoring dashboard before Slack each morning since 'pipeline failures often surface overnight' — daytime work frequently fragmented by reactive firefighting.
A day in this role
Expect to build and maintain ETL/ELT pipelines (batch and increasingly streaming), design warehouse/lakehouse schemas for cost and query performance, and treat data-quality checks and freshness monitors as first-class engineering work, not an afterthought — data-quality ownership shows up in roughly half of all postings. The other constant is people, not pipelines: translating what analysts, data scientists, and PMs actually need into pipeline and schema requirements is close to universal — it's in nearly every posting in the space, not just boilerplate "collaborative team player" language. A first-person practitioner account describes a task that "should take a day" — adding one new event stream — actually taking three weeks once schema discovery, Spark/JVM debugging, and cluster-contention troubleshooting were included, with time split roughly evenly across discovery, development, debugging, and production operations. Many data engineers check a monitoring dashboard before Slack each morning, since pipeline failures often surface overnight, and afternoons skew meeting-heavy (analytics syncs, PM planning, schema discussions) rather than pure build time. Increasingly, some of the actual build work is AI-assisted: using AI coding tools day to day, or building the pipelines and feature stores that feed ML/LLM systems downstream. One honest caveat on the operational-load framing: formal on-call/pager rotations are spelled out explicitly in only a small slice of job postings' text, even though monitoring and reliability ownership is a constant theme — so "you'll get paged sometimes" is a real but unevenly-disclosed part of the job, not something every posting will flag upfront. A widely cited (if single-source) survey found 97% of data engineers report burnout, with manual firefighting and being blamed for upstream data problems as the top drivers — worth taking as a real signal about the complaint pattern, even if the exact percentage shouldn't be treated as gospel.
Comp structure
Typical: $184K
Base + bonus + equity at most tech companies, overwhelmingly salaried rather than commission-based — this is a build/operate role, not a revenue-attributed one. Levels.fyi aggregate median total comp sits around $160K, but the spread by company is large: Capital One's median total comp is around $130K versus Netflix's roughly $565K, a gap driven almost entirely by equity rather than base. Current postings back up that spread on the base-salary side too: senior/staff roles at companies like CoreWeave, Discord, and Faire currently list $182K-$313K base, while more standard mid-level roles cluster lower — Disney's senior data engineer posting lists $142K-$190K, FanDuel's (non-senior) data engineer posting lists $125K-$164K, and Ibotta and Brex both list entry-to-mid roles in the $110K-$165K range. Consulting/managed-services shops (e.g., 3Cloud-style) skew toward base plus a smaller bonus, with less equity than product companies.
▸ Full compensation breakdown by level and company tier▾ Full compensation breakdown by level and company tier
Compensation by Company Tier
Total compensation (base + bonus + annualized equity) across five company tiers, at each career level. The same role pays very differently depending on where you take it.
data-engineer · total comp (base + bonus + annualized equity) · P25–P75 band, P50 median
Equity Reality Check
The guaranteed money (base + bonus) against the equity upside. Startup equity is illiquid — the equity figure is annualized paper value at vest, not cash in hand.
Examples of real job postings
snapshot from 2026-07-12Real postings from the research corpus behind this archetype. Click one to read the actual listing.
How to test this cheaply
Try building a small end-to-end pipeline yourself — pull data from a public API, land it in a warehouse (BigQuery/Snowflake free tier), add a basic freshness/schema-drift check, and let it run unattended for a week to see how you react when something breaks overnight.
If you know a data engineer, ask to see their actual on-call runbook or their last postmortem — it'll tell you more about the real operational load than any job posting will.
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